Para quem ainda tem dúvidas que em breve termos modelos de Machine Learning em nossos dispositivos móveis para identificar diversos comportamentos como andar, estar movimento em um veículo automotor, ou mesmo em situações de buffer (i.e. filas, ou outras situações que estamos parados) esse paper mostra um ótimo caminho de implementação.
Abstract: Recognition of human activities, using smart phones and wearable devices, has attracted much attention recently. The machine learning (ML) approach to human activity recognition can broadly be classified into two categories: training an ML model on (i) an impersonal dataset or (ii) a personal dataset. Previous research shows that models learned from personal datasets can provide better activity recognition accuracy compared to models trained on impersonal datasets. In this paper, we develop a hybrid incremental (HI) method with logistic regression models. This method uses incremental learning of logistic regression to combine the advantages of the impersonal and personal approaches. We investigate two essential issues for this method, which are the selection of the learning rate schedule and the class imbalance problem. Our experiments show that the models learned using our HI method give better accuracy than the models learned from personal or impersonal data only. Besides, the techniques of adaptive learning rate and cost-sensitive learning generally give faster updates and more robust ML models in incremental learning. Our method also has potential bene- fits in the area of privacy preservation.
Conclusions: In this paper, we propose a novel hybrid incremental (HI) method for activity recognition. Traditionally, activity recognition models have been trained on either impersonal or personal datasets. Our HI method effectively combines the advantages of these two approaches. After learning a model on an impersonal dataset in servers, the mobile devices can apply incremental learning on the model using personal data. We focus on logistic regression due to its several benefits, including its small model size that saves bandwidth, good performance in activity recognition, and easy incremental update. We address two important problems that are likely to arise in practical implementations of this incremental learning task. The first problem is associated with user diversity, making it very difficult to tune the learning-rate for each user. The second issue is related to personal data being so imbalanced at times that it may spoil the impersonal model. To overcome those problems, we applied an adaptive learning rate and a cost-sensitive technique. Finally, experimental results are used to validate our solutions.